SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 501–550 of 1718 papers

TitleStatusHype
Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning—0
Empathic Coupling of Homeostatic States for Intrinsic Prosociality—0
Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control—0
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach—0
Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel Machine Scheduling—0
A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs—0
Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing—0
OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement LearningCode0
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions—0
Anytime-Constrained Equilibria in Polynomial Time—0
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks—0
Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement LearningCode0
Multi-Agent Reinforcement Learning with Selective State-Space Models—0
Evolutionary Dispersal of Ecological Species via Multi-Agent Deep Reinforcement Learning—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
Scalable spectral representations for multi-agent reinforcement learning in network MDPs—0
Evolution of Societies via Reinforcement LearningCode0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL—0
A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization—0
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games—0
Variational Inequality Methods for Multi-Agent Reinforcement Learning: Performance and Stability Gains—0
Large Legislative Models: Towards Efficient AI Policymaking in Economic SimulationsCode0
OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous VehiclesCode0
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning—0
Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile RobotsCode0
Last Iterate Convergence in Monotone Mean Field Games—0
Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model—0
Sable: a Performant, Efficient and Scalable Sequence Model for MARL—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank—0
Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning—0
Enabling Multi-Robot Collaboration from Single-Human Guidance—0
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training—0
Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems—0
Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning—0
PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach—0
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling—0
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks—0
HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement LearningCode0
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified